| SemPT: Semantic Prompt Tuning for Vision-Language Models | | 59.48 | PromptKD w/ SemPT | 2025-08-14 |
| Decouple before Align: Visual Disentanglement Enhances Prompt Tuning | ✓ Link | 59.07 | DAPT + PromptKD | 2025-08-01 |
| Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter | | 51.78 | VRGAdapter | 2025-07-14 |
| AmPLe: Supporting Vision-Language Models via Adaptive-Debiased Ensemble Multi-Prompt Learning | ✓ Link | 50.77 | MMRL + AmPLe | 2025-12-20 |
| Prompt Tuning for CLIP on the Pretrained Manifold | | 50.63 | ManiPT | 2026-02-22 |
| AmPLe: Supporting Vision-Language Models via Adaptive-Debiased Ensemble Multi-Prompt Learning | ✓ Link | 50.57 | MMA + AmPLe | 2025-12-20 |
| MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models | | 50.40 | MAIL++ | 2026-05-25 |
| AmPLe: Supporting Vision-Language Models via Adaptive-Debiased Ensemble Multi-Prompt Learning | ✓ Link | 50.37 | MaPLe + AmPLe | 2025-12-20 |
| Gram-Anchored Prompt Learning for Vision-Language Models via Second-Order Statistics | | 50.13 | GAPL | 2026-04-05 |
| Decouple before Align: Visual Disentanglement Enhances Prompt Tuning | ✓ Link | 49.98 | DAPT + PromptSRC | 2025-08-01 |
| Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition | ✓ Link | 49.8 | POMP | 2023-04-10 |
| VaMP: Variational Multi-Modal Prompt Learning for Vision-Language Models | | 49.69 | VaMP | 2025-11-27 |
| MQAdapter: Multi-Modal Quantum Adapter for Coarse-to-Fine VLM Fine-tuning | | 49.65 | PromptSRC + MQAdapter | 2026-07-14 |
| HeBA: Heterogeneous Bottleneck Adapters for Robust Vision-Language Models | ✓ Link | 49.57 | HeBA | 2026-03-17 |
| MQAdapter: Multi-Modal Quantum Adapter for Coarse-to-Fine VLM Fine-tuning | | 49.56 | MMRL++ + MQAdapter | 2026-07-14 |
| Self-regulating Prompts: Foundational Model Adaptation without Forgetting | ✓ Link | 49.55 | PromptSRC | 2023-07-13 |
| ProCAP: Probabilistic Cross-Attentive Prompt Learning for Vision-Language Models | ✓ Link | 49.53 | ProCAP | 2026-09-24 |
| RMAdapter: Reconstruction-based Multi-Modal Adapter for Vision-Language Models | | 49.50 | RMAdapter | 2025-12-07 |
| DeAR: Fine-Grained VLM Adaptation by Decomposing Attention Head Roles | ✓ Link | 49.50 | DeAR | 2026-03-01 |
| Hierarchical Cross-modal Prompt Learning for Vision-Language Models | ✓ Link | 49.47 | HiCroPL | 2025-07-20 |
| Consistency-guided Prompt Learning for Vision-Language Models | ✓ Link | 49.43 | CoPrompt | 2023-06-01 |
| Decouple before Align: Visual Disentanglement Enhances Prompt Tuning | ✓ Link | 49.43 | DAPT-S | 2025-08-01 |
| DiscoVL: Unveiling Disentangled C ross-Modal Representation Learning via Orthogonal Adversarial Regularization for V ision-Language Models | | 49.40 | DiscoVL | 2026-10-08 |
| Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models | ✓ Link | 49.36 | HPT | 2023-12-11 |
| MQAdapter: Multi-Modal Quantum Adapter for Coarse-to-Fine VLM Fine-tuning | | 49.33 | MaPLe + MQAdapter | 2026-07-14 |
| HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling | ✓ Link | 49.28 | HPT++ | 2024-08-27 |
| MMRL: Multi-Modal Representation Learning for Vision-Language Models | ✓ Link | 49.17 | MMRL | 2025-03-11 |
| MaPLe: Multi-modal Prompt Learning | ✓ Link | 49.15 | MaPLe | 2022-10-06 |
| Robust Prompt Tuning for Vision-Language Models with Mild Semantic Noise | | 49.13 | ANPrompt | 2025-08-06 |
| MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation | ✓ Link | 49.07 | MMLoP | 2026-02-24 |
| Decouple before Align: Visual Disentanglement Enhances Prompt Tuning | ✓ Link | 48.82 | DAPT-G | 2025-08-01 |
| Conditional Prompt Learning for Vision-Language Models | ✓ Link | 48.75 | CoCoOp | 2022-03-10 |
| Learning Transferable Visual Models From Natural Language Supervision | ✓ Link | 46.15 | CLIP | 2021-02-26 |